This paper presents an intelligent modeling approach to individual thermal comfort and energy optimization problem, which aims to minimize energy consumption and improve thermal environmental conditions for human occupancy. In our previous study, this optimization problem was solved under the assumption of the existence of information about the thermal comfort preferences of individuals. A traditional optimization method is used to calculate off-line optimum solutions to this problem at numerous operating points. These solutions are used to train an intelligent system such as a fuzzy logic system under the same assumption resulting in a control system which encapsulates the behavior of the collection of optimum solutions. This methodology is named "Intelligent Modeling of Optimized Systems" (IMOS) in this paper. However, it is hard to gather information about individuals' thermal comfort preferences in practice. The sensitivity analysis on the optimization problem and its approximation with fuzzy logic system regarding individual thermal comfort preferences has been investigated in this paper.